The detection of the concentration level of glucose or other analytes in certain individuals may be vitally important to their health. For example, the monitoring of glucose levels is particularly important to individuals with diabetes or pre-diabetes. People with diabetes may need to monitor their glucose levels to determine when medication (e.g., insulin) is needed to reduce their glucose levels or when additional glucose is needed.
Devices have been developed for automated in vivo monitoring of analyte time series characteristics, such as glucose levels, in bodily fluids such as in the blood stream or in interstitial fluid. Some of these analyte level measuring devices are configured so that at least a portion of a sensor of an on-body device is positioned below a skin surface of a user, e.g., in a blood vessel or in the subcutaneous tissue of a user. As used herein, the term analyte monitoring system is used to refer to any type of in vivo monitoring system that uses a sensor disposed with at least a subcutaneous portion to measure and store sensor data representative of analyte concentration levels automatically over time. Analyte monitoring systems include both (1) systems such as continuous glucose monitors (CGMs) which transmit sensor data continuously or at regular time intervals (e.g., once per minute) to a processor/display unit and (2) systems that transfer stored sensor data in one or more batches in response to data request from a processor/display unit (e.g., based on an activation action and/or proximity using, for example, a near field communications protocol).
Some analyte monitoring systems may store samples relatively infrequently. For example, the sensor data may only include measurements or samples taken once every ten or fifteen minutes. In some cases, such sparsely sampled analyte sensor data may not accurately reflect the analyte concentration levels, particularly if signal noise is present. Thus, what are needed are systems, methods and apparatus that can reliably represent the analyte concentration level even if sparsely sampled data is used.